5G introduced modularized network functions (NFs) to support emerging services in a more flexible and elastic manner. To mitigate the complexity in such modularized NF management, automated network operation and management are indispensable, and thus the 3rd generation partnership project (3GPP) has introduced a network data analytics function (NWDAF). However, a conventional NWDAF needs to conduct both inference and training tasks, and thus it is difficult to provide the analytics results to NFs in a timely manner for an increased number of analytics requests. In this article, we propose a hierarchical network data analytics framework (H-NDAF) where inference tasks are distributed to multiple leaf NWDAFs and training tasks are conducted at the root NWDAF. Extensive simulation results using open-source software (i.e., free5GC) demonstrate that H-NDAF can provide sufficiently accurate analytics and faster analytics provision time compared to the conventional NWDAF.

通过引入5G模块化网络功能和分布式结构，我们提出了一种分层网络数据分析框架(H-NDAF)，以解决在增加的分析请求中，传统网络数据分析功能不能及时提供准确结果的问题。通过广泛的仿真结果，在开源软件(free5GC)中证明了H-NDAF相对于传统NWDAF可以提供更准确和更快速的分析结果。

B5G网络自动化的分层网络数据分析框架：设计与实现